LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects.  DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications. 

LLNL researchers have developed a method to enhance the performance of polyelectrolyte membranes by using a humidity-controlled crosslinking process which can be applied to precisely adjust the water channels of the membrane.

LLNL has developed a method of extending device lifetimes by imprinting into the device a shape that excludes specific vibrational modes, otherwise known as a phononic bandgap. Eliminating these modes prevents one of the primary energy loss pathways in these devices. LLNL’s new method enhances the coherence of superconducting circuits by introducing a phononic bandgap around the system’s…

The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…

LLNL has a patented process to produce colloidal silica directly from geothermal fluids. Livermore’s process uses membranes to produce a mono-dispense slurry of colloidal silica particles for which there are several applications. LLNL has demonstrated that colloidal silica solutions that result from extraction of silica from geothermal fluids undergo a transition to a solid gel over a range of…